Shader-based Automatic Camera Layout Optimization for Mobile Robots using Genetic Algorithm

Given a mobile robot and a certain number of cameras, this paper addresses the problem of finding locations and orientations of the cameras relative to the robot such that an optimality criteria is maximized. The optimality criteria designed in this paper emphasizes the trade-off between the coverage of area of interest around the robot by the cameras subject to occlusion constraints and the proximity of cameras to the robot structure. Real coded genetic algorithm is employed to search for such optimal layout and the optimality criteria serves as the fitness function. The computation intensive parts, namely the coverage and proximity analysis, are adapted to such a form that GPU with programmable shader can be accommodated to accelerate them. A graphical user interface tool is constructed to allow observation and checks during the optimization process. Promising results are displayed in an experiment concerning a truck with seven cameras. The optimization framework outlined in this paper can also be extended to optimize layout of scanning sensors like LiDAR and Radar mounted on arbitrary structures.